Award Abstract # 1911191
CNS Core: Small: RUI: Optimal and Efficient Resource Allocation in Policy-Driven Data Centers: A Network Flow Approach

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: CALIFORNIA STATE UNIVERSITY, DOMINGUEZ HILLS FOUNDATION
Initial Amendment Date: September 13, 2019
Latest Amendment Date: September 13, 2019
Award Number: 1911191
Award Instrument: Standard Grant
Program Manager: Ann Von Lehmen
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2019
End Date: September 30, 2023 (Estimated)
Total Intended Award Amount: $354,291.00
Total Awarded Amount to Date: $354,291.00
Funds Obligated to Date: FY 2019 = $354,291.00
History of Investigator:
  • Bin Tang (Principal Investigator)
    btang@csudh.edu
Recipient Sponsored Research Office: California State University-Dominguez Hills Foundation
1000 E VICTORIA ST
CARSON
CA  US  90747-0001
(310)243-2852
Sponsor Congressional District: 44
Primary Place of Performance: California State University-Dominguez Hills
1000 E Victoria St
Carson
CA  US  90747-0001
Primary Place of Performance
Congressional District:
44
Unique Entity Identifier (UEI): MWEPWP3T6XL5
Parent UEI:
NSF Program(s): Networking Technology and Syst
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7923, 9229
Program Element Code(s): 736300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Emerging data- and communication-intensive applications such as online video streaming, social media, health and medical applications, scientific applications including high-energy physics and bioinformatics, and educational applications (such as the rapidly-growing Massive Open Online Courses) are all enabled by large cloud data centers. However, this underpinning infrastructure is increasingly stressed by the growing complexities of managing data center resources. This is evidenced by the frequent outages of cloud services from leading tech companies, including Amazon Cloud Storage and Microsoft Skype, and popular mobile apps such as Gmail and Whatsapp. To address this challenge, this project will create an optimal and efficient resource allocation framework for policy driven data centers (PDDCs), to manage cloud user applications and cloud resources (i.e., servers, networks, and power) in an integrated fashion.

The goal of this project is to integrate compute, data, and middleboxes (MBs), three building blocks of PDDCs, into one framework to achieve optimal cloud resource management. A variety of important problems in PDDCs, including virtual machine (VM) migration and placement, load balancing, flow priority and fault tolerance can all be solved using network flow techniques that provide optimal and efficient resource allocation solutions. In particular, the project identifies a series of new policy-preserving problems that adaptively coordinate compute, data, and MBs, and invents a suite of policy-preserving algorithms that satisfy diverse cloud policies while consuming cloud resources efficiently. The proposed techniques include placing, migrating, replicating, and traffic engineering compute, data, and MBs in the PDDC. The project will compare results with integer linear programming (ILP)-based solutions and extend the approach to multi-objective optimization problems. Expected outcomes are fundamental theories, architectures, algorithms, and protocols for the PDDCs, and prototypes that provide long term policy-preserving cloud services.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Aguilera, Phillip and Gonzalez, Christopher and Tang, Bin "Achieving Virtual Network Function Load-Balanced Flow Migration in Dynamic Cloud Data Centers" Proceedings of the First Computer Science Conference for CSU Undergraduates (CSCSU 2021). , 2021 Citation Details
Aguilera, Phillip and Gonzalez, Christopher and Tang, Bin "FMDV: Dynamic Flow Migration in Virtual Network Function-Enabled Cloud Data Centers" IEEE International Conference on Communications (ICC 2022) , 2022 https://doi.org/10.1109/ICC45855.2022.9839019 Citation Details
Flores, Hugo and Tran, Vincent and Tang, Bin "PAM & PAL: Policy-Aware Virtual Machine Migration and Placement in Dynamic Cloud Data Centers" IEEE Conference on Computer Communications (INFOCOM 2020) , 2020 https://doi.org/10.1109/INFOCOM41043.2020.9155472 Citation Details
Gonzalez, Christopher and Tang, Bin "FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers" International Conference on Computer Communications and Networks (ICCCN 2020) , 2020 https://doi.org/10.1109/ICCCN49398.2020.9209676 Citation Details
Gonzalez, Christopher and Tang, Bin "Performance Comparison of Fault-Tolerant Virtual Machine Placement Algorithms in Cloud Data Centers" the First Computer Science Conference for CSU Undergraduates (CSCSU 2021) , 2021 Citation Details
Hsu, Shanglin and Yu, Yuning and Tang, Bin "DRE2: Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming" IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2020) , 2020 Citation Details
Hsu, Shanglin and Yu, Yuning and Tang, Bin "DRE 2 : Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming Approach" 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) , 2020 https://doi.org/10.1109/MASS50613.2020.00019 Citation Details
Lutz, Jeff and Tang, Bin and Gonzalez, Christopher "Throughput Maximization of Virtual Machine Communications in Bandwidth-Constrained Data Centers" IEEE Global Communications Conference (GLOBECOM 2021) , 2021 https://doi.org/10.1109/GLOBECOM46510.2021.9686028 Citation Details
Lynn Gao, Yutian Chen "15th ACM International Conference on Underwater Networks & Systems (WUWNET 2021)" 11th EAI International Conference on Game Theory for Networks (GameNets 2021). , 2021 Citation Details
Rodicio, Enrique and Pan, Deng and Liu, Jason and Tang, Bin "Achieving High End-to-End Availability in VNF Networks" International Conference on Computer Communications and Networks (ICCCN 2022) , 2022 https://doi.org/10.1109/ICCCN54977.2022.9868897 Citation Details
Tran, Vincent and Sun, Jingsong and Tang, Bin and Pan, Deng "Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers" 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022) , 2022 https://doi.org/10.1109/IPDPS53621.2022.00094 Citation Details
(Showing: 1 - 10 of 11)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The goal of this project is to create an optimal and efficient resource allocation framework for policy driven data centers (PDDCs), to manage cloud user applications and cloud resources (i.e., servers, networks, and power) in an integrated fashion. Cloud data centers provides the infrastructure to (and underpines) many modern information technology applications such as online video streaming, social media, health and medical applications, scientific applications including high-energy physics and bioinformatics, and educational applications (such as the rapidly-growing Massive Open Online Courses). However, this underpinning infrastructure is increasingly stressed by the growing complexities of managing data center resources.

In PDDCs, network devices called middleboxes (MBs) or virtual network functions (VNFs) are introduced inside the data centers to achieve performance and security guarantees for the data center applications. One challenge is how to integrate compute, data, and three building blocks of PDDCs, into one framework to achieve optimal cloud resource management.

In this project we mainly take a network flow perspective that models the information flow in PDDCs as flows in flow networks. We show that a variety of important problems in PDDCs, including virtual machine (VM) migration and placement, load balancing, flow priority and fault tolerance can all be solved using network flow techniques that provide optimal and efficient resource allocation solutions. In particular, the project identified and solved a series of new policy-preserving problems that adaptively coordinate compute, data, and MBs, and invents a suite of policy-preserving algorithms that satisfy diverse cloud policies while consuming cloud resources efficiently. The proposed techniques include placing, migrating, replicating, and traffic engineering compute, data, and MBs in the PDDC. The outcomes include a few new theories, algorithms, and protocols for the PDDCs, published as ten reputable conference paper including Infocom, IPDPS, ICC, and Globecom.


Last Modified: 10/31/2023
Modified by: Bin Tang

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